Facial Expression Recognition Using Pre-trained Architectures

Title

Facial Expression Recognition Using Pre-trained Architectures

Creator

Reghunathan R.K.; Ramankutty V.K.; Kallingal A.; Vinod V.

Description

In the area of computer vision, one of the most difficult and challenging tasks is facial emotion recognition. Facial expression recognition (FER) stands out as a pivotal focus within computer vision research, with applications in various domains such as emotion analysis, mental health assessment, and humancomputer interaction. In this study, we explore the effectiveness of ensemble methods that combine pre-trained deep learning architectures, specifically AlexNet, ResNet50, and Inception V3, to enhance FER performance on the FER2013 dataset. The results from this study offer insights into the potential advantages of ensemble-based approaches for FER, demonstrating that combining pre-trained architectures can yield superior recognition outcomes. 2024 by the authors.

Source

Engineering Proceedings, Vol-62, No. 1

Date

2024-01-01

Publisher

Multidisciplinary Digital Publishing Institute (MDPI)

Subject

CNN; facial expression recognition; FER2013; pre-trained architectures

Coverage

Reghunathan R.K., Department of Computer Science, CHRIST University, Bangalore, 560029, India; Ramankutty V.K., Department of Computer Science, CHRIST University, Bangalore, 560029, India; Kallingal A., Department of Computer Science, CHRIST University, Bangalore, 560029, India; Vinod V., Idea Elan India Pvt. Ltd., Hyderabad, 500082, India

Rights

All Open Access; Hybrid Gold Open Access

Relation

ISSN: 26734591

Format

Online

Language

English

Type

Article

Identifier

https://doi.org/10.3390/engproc2024062022

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85205319559&doi=10.3390%2fengproc2024062022&partnerID=40&md5=b88602bde0a03c9097bba9abb0a1183f